Tuberculosis (TB) remains a formidable global health challenge, driven by complex spatiotemporal transmission dynamics and influenced by factors such as population mobility and behavioral changes. We propose an Epidemic-Guided Deep Learning (EGDL) approach that fuses mechanistic epidemiological principles with advanced deep learning techniques to enhance early warning systems and intervention strategies for TB outbreaks. Our framework is built upon a networked Susceptible-Infectious-Recovered (SIR) model augmented with a saturated incidence rate and graph Laplacian diffusion, capturing both long-term transmission dynamics and region-specific population mobility patterns. Compartmental model parameters are rigorously estimated using Bayesian inference via the Markov Chain Monte Carlo (MCMC) approach. Theoretical analysis leveraging the comparison principle and Green's formula establishes global stability properties of the disease-free and endemic equilibria. Building on these epidemiological insights, we design two forecasting architectures, EGDL-Parallel and EGDL-Series, that integrate the mechanistic outputs of the networked SIR model within deep neural networks. This integration mitigates the overfitting risks commonly encountered in data-driven methods and filters out noise inherent in surveillance data, resulting in reliable forecasts of real-world epidemic trends. Experiments conducted on TB incidence data from 47 prefectures in Japan demonstrate that our approach delivers robust and accurate predictions across multiple time horizons (short to medium-term forecasts). Additionally, incorporating uncertainty quantification through conformal prediction enhances the model's practical utility for guiding targeted public health interventions.

翻译：结核病（TB）仍然是全球健康面临的严峻挑战，其传播受复杂的时空动态驱动，并受到人口流动和行为变化等因素的影响。我们提出了一种流行病学引导的深度学习（EGDL）方法，该方法将机制性流行病学原理与先进的深度学习技术相融合，以增强结核病暴发的早期预警系统和干预策略。我们的框架建立在网络化易感-感染-恢复（SIR）模型基础上，该模型通过饱和发病率与图拉普拉斯扩散进行增强，能够捕捉长期传播动态和区域特定的人口流动模式。我们通过马尔可夫链蒙特卡洛（MCMC）方法进行贝叶斯推断，严格估计了仓室模型的参数。利用比较原理和格林公式的理论分析，确立了无病平衡点和地方病平衡点的全局稳定性性质。基于这些流行病学洞见，我们设计了两种预测架构——EGDL-Parallel 和 EGDL-Series，它们将网络化SIR模型的机制性输出整合到深度神经网络中。这种整合减轻了数据驱动方法中常见的过拟合风险，并过滤了监测数据中固有的噪声，从而实现对现实世界疫情趋势的可靠预测。在日本47个都道府县的结核病发病率数据上进行的实验表明，我们的方法能够在多个时间范围（短期至中期预测）内提供稳健且准确的预测。此外，通过保形预测纳入不确定性量化，增强了模型在指导针对性公共卫生干预方面的实际效用。